{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:32:30Z","timestamp":1774121550691,"version":"3.50.1"},"reference-count":41,"publisher":"Institution of Engineering and Technology (IET)","issue":"3","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007446","name":"King Khalid University","doi-asserted-by":"publisher","award":["PNURSP2024R506"],"award-info":[{"award-number":["PNURSP2024R506"]}],"id":[{"id":"10.13039\/501100007446","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CAAI Trans on Intel Tech"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Breast cancer is one of the major causes of deaths in women. However, the early diagnosis is important for screening and control the mortality rate. Thus for the diagnosis of breast cancer at the early stage, a computer\u2010aided diagnosis system is highly required. Ultrasound is an important examination technique for breast cancer diagnosis due to its low cost. Recently, many learning\u2010based techniques have been introduced to classify breast cancer using breast ultrasound imaging dataset (BUSI) datasets; however, the manual handling is not an easy process and time consuming. The authors propose an EfficientNet\u2010integrated ResNet deep network and XAI\u2010based framework for accurately classifying breast cancer (malignant and benign). In the initial step, data augmentation is performed to increase the number of training samples. For this purpose, three\u2010pixel flip mathematical equations are introduced: horizontal, vertical, and 90\u00b0. Later, two pre\u2010trained deep learning models were employed, skipped some layers, and fine\u2010tuned. Both fine\u2010tuned models are later trained using a deep transfer learning process and extracted features from the deeper layer. Explainable artificial intelligence\u2010based analysed the performance of trained models. After that, a new feature selection technique is proposed based on the cuckoo search algorithm called cuckoo search controlled standard error mean. This technique selects the best features and fuses using a new parallel zero\u2010padding maximum correlated coefficient features. In the end, the selection algorithm is applied again to the fused feature vector and classified using machine learning algorithms. The experimental process of the proposed framework is conducted on a publicly available BUSI and obtained 98.4% and 98% accuracy in two different experiments. Comparing the proposed framework is also conducted with recent techniques and shows improved accuracy. In addition, the proposed framework was executed less than the original deep learning models.<\/jats:p>","DOI":"10.1049\/cit2.12385","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T13:11:14Z","timestamp":1728393074000},"page":"842-857","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["An EfficientNet integrated ResNet deep network and explainable AI for breast lesion classification from ultrasound images"],"prefix":"10.1049","volume":"10","author":[{"given":"Kiran","family":"Jabeen","sequence":"first","affiliation":[{"name":"Department of Computer Science HITEC University Taxila  Rawalpindi Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6347-4890","authenticated-orcid":false,"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science HITEC University Taxila  Rawalpindi Pakistan"}]},{"given":"Ameer","family":"Hamza","sequence":"additional","affiliation":[{"name":"Department of Computer Science HITEC University Taxila  Rawalpindi Pakistan"}]},{"given":"Hussain Mobarak","family":"Albarakati","sequence":"additional","affiliation":[{"name":"Computer and Network Engineering Department College of Computing Umm Al\u2010Qura University  Makkah Saudi Arabia"}]},{"given":"Shrooq","family":"Alsenan","sequence":"additional","affiliation":[{"name":"Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University  Riyadh Saudi Arabia"}]},{"given":"Usman","family":"Tariq","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems College of Business Administration Prince Sattam bin Abdul Aziz University  Alkharaj Saudi 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